# Use this R-Chunk to import all your datasets!
Afghanistan <- read_excel("C:/Users/Admin/Downloads/newData.xlsx") %>%
rename(Strike_type = `Type of attack`) %>%
mutate(date =`Date (MM-DD-YYYY)`) %>%
separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% select(-...14,-...15)
Somalia <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Somalia") %>%
rename(Strike_type = `Strike type`) %>%
mutate(date =`Date (MM-DD-YYYY)`) %>%
separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>%
select(-...29,-...31)
Yemen <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Yemen") %>%
rename(Strike_type = `Type of attack`) %>%
mutate(date = `Date (MM-DD-YYYY)`) %>%
separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% select(-...30)
Pakistan <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Pakistan") %>%
mutate(Strike_type = "Drone") %>%
mutate(date = `Date (MM-DD-YYYY)`) %>%
separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>%
select(- ...13,- ...14, -...24 )
Afghanistan <- purrr::map(Afghanistan, as.character)
Somalia <- purrr::map(Somalia, as.character)
Pakistan <- purrr::map(Pakistan, as.character)
Yemen <- purrr::map(Yemen, as.character)
data <- bind_rows(Afghanistan, Somalia, Yemen, Pakistan)
drone_data <- data
ds <- drone_data %>%
filter(str_detect(Strike_type, "rone"))
drone_strikes1 <- ds %>%
mutate(`Most Specific Lat/Long` = ifelse(`Most Specific Lat/Long` == "Unknown", `Lat/Long`, `Most Specific Lat/Long` ))
Af <- Afghanistan %>% data.frame(Afghanistan)
# mean of civillians killed per strike Afghanistan
Afghanistan1 <- as.data.frame(Afghanistan)
af_casulaties <- Afghanistan1 %>%
filter(str_detect(Strike_type, "rone")) %>%
group_by(Country) %>%
summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)), total_child_deaths = sum(as.numeric(Maximum.children.reported.killed)))
# af casualty per year
af_cas_yr <- Afghanistan1 %>%
filter(str_detect(Strike_type, "rone")) %>%
group_by(Country,year) %>%
summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Maximum.children.reported.killed)))
## Somalia Drone strike statisctics
Somalia1 <- as.data.frame(Somalia) %>%
filter(str_detect(Strike_type, "rone"))
S_casualties <- Somalia1 %>%
group_by(Country) %>%
summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.killed)), total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.killed))))
# somalia casualties per year
S_cas_yr <- Somalia1 %>%
group_by(Country,year) %>%
summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.killed)), childen_casualties = sum(as.numeric(Maximum.children.killed)))
## pakistan drone statistics
Pakistan1 <- as.data.frame(Pakistan) %>%
filter(str_detect(Strike_type, "rone"))
P_casualties <- Pakistan1 %>%
group_by(Country) %>%
summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Pakistan1$Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)),total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.reported.killed))))
# Pak casualties per yer
P_casualties_year <- Pakistan1 %>%
group_by(Country,year) %>%
summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Pakistan1$Maximum.children.reported.killed)))
# Yemen Drone statistics
Yemen1 <- as.data.frame(Yemen) %>%
filter(str_detect(Strike_type, "rone"))
Y_casualites <- Yemen1 %>%
group_by(Country) %>%
summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)), total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.reported.killed))))
# yemen casualties per year
Y_cas_yr <- Yemen1 %>%
group_by(Country,year) %>%
summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Maximum.children.reported.killed)))
# total casualties per strike per country visualization
t_casualties <- bind_rows(af_casulaties, S_casualties, Y_casualites, P_casualties)
cas_per_yr <- bind_rows(af_cas_yr, S_cas_yr, Y_cas_yr, P_casualties_year)
setZoom = function(map, zoom, options = list()) {
view = list(zoom, options)
dispatch(map,
"setZoom",
leaflet = {
map$x$setZoom = view
map$x$fitBounds = NULL
map
},
leaflet_proxy = {
invokeRemote(map, "setZoom", view)
map
}
)
}
Between 2010 and 2020 the Bureau of Investigative Journalism collected data on US strikes in Afghanistan, Pakistan, Somalia and Yemen from government, military and intelligence officials, and from credible media, academic and other sources, including Bureau researchers. Here in the data set, the Bureau presents quantitative data on strikes and casualty estimates in spreadsheets, and qualitative data in narrative timelines.
This analysis is based upon the statistics the Bureau have documented at this present time. The data has been filtered to confirmed drone strikes or possible drone strikes, it includes other kinds of military air strikes but the focus is on drone strikes.
Source: https://dronewars.github.io/data/
Source: https://WWW.investigates.com/stories/2017-01-01/drone-wars-the-full-data
# Use this R-Chunk to clean & wrangle your data!
drone_strikes1 %>%
mutate(Count = n()) %>%
ggplot(aes(x = President, y = ..count.. , fill = President)) +
geom_bar() + theme_bw() + facet_wrap(vars(Country),scales = "free_y") + geom_text(stat='count', aes(label=..count..), vjust=1, col = "black") + labs(title = "Number of Drone Strikes Per Country by Presidency", y = "Drone Strikes")
The bar charts shown shows the number of Drone Strikes for each presidential term in the data set. Obama's presidential term had more drone strikes than any of the other presidencies.
map_drone_strikes <- drone_strikes1 %>%
mutate("Most_Specific_Lat/Long" = `Most Specific Lat/Long`) %>%
separate(`Most Specific Lat/Long`, sep = ",", into = c("final_lat","final_lon")) %>%
relocate(final_lat,final_lon) %>%
filter(final_lat > 0 & final_lon > 0) %>%
group_by(`Most Specific Location`) %>%
mutate(Count = n())
leaflet() %>% addTiles() %>%
setView(lng = 55, lat = 42.5510, zoom = 3) %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>%
setZoom(zoom = .1) %>%
addMarkers(data = map_drone_strikes,
lng = ~as.numeric(final_lon),
lat = ~as.numeric(final_lat),
label = ~paste(`Most Specific Location`, "=", Count, "Drone Strike(s)"),
clusterOptions = markerClusterOptions()) %>%
addRectangles(lng1 = 40.98105, -1.68325, lng2 = 51.13387, 12.02464, fillColor = "transparent") %>% # somalia
addRectangles(lng1 = 60.8742484882, 23.6919650335, lng2 = 77.8374507995, 37.1330309108, fillColor = "transparent") %>% #Pakistan
addRectangles(lng1 = 42.6048726743, 12.5859504257, lng2 = 53.1085726255, 19.0000033635, fillColor = "transparent")
This interactive map presents specific drone strike locations. Most notable is the Waziristan District in Pakistan. The district has over ninety drone strikes located in the area. (Some locations do not have updated landscapes and therefore, land features of drone strike area's may not appear when zoomed upon)
area <- drone_strikes1 %>%
group_by(`Most Specific Location`, Country) %>%
summarise(Number_of_Strikes = n()) %>%
rename("Location" = `Most Specific Location`) %>%
filter(!Location %in% c("Unknown", "unknown")) %>%
ungroup() %>%
slice_max(Number_of_Strikes, n = 10)
area %>% ggplot(aes(x = fct_reorder(Location, Number_of_Strikes), y = Number_of_Strikes)) +
geom_col(fill = "Orange") + geom_text(aes(label = Number_of_Strikes), vjust = -0.5, hjust=-.1) +
labs(title = "Ten Most Targeted Locations", x ="Locations" , y ="Number of Strikes") + theme_bw() +
coord_flip()
The bar chart above shows the top ten locations that have the most drone strikes. Bayda, Yemen has had the most drone strikes.
drone_strikes1 %>% ggplot(aes(x = fct_inorder(Country), fill = Country)) +
geom_bar( stat = "count") + geom_text(stat='count', aes(label=..count..), vjust=-.3,col = "black") +
labs(title = "Total Drone Strikes per country", x = "Counry", y ="Drone Strikes") + theme_bw()
From this bar chart it is clear that Pakistan has had the most drone strikes happen than any of the other countries. The strikes in Pakistan are under the command of the CIA rather than the military. Unlike in Yemen and Somalia where the military and CIA work together.
drone_strikes1 <- drone_strikes1 %>% mutate(Year = as.numeric(year))
c <- drone_strikes1 %>%
group_by(year) %>%
mutate(count = n()) %>%
ggplot(aes(x = Year, y = count, col = Country)) +
geom_point() +
geom_line() +
facet_wrap(~Country) + labs(title = "(Interactive) Drone strikes per year", x = "Country", y = "Drone Strikes") + theme_bw()
ggplotly(c)
From the graphs above you can hover your mouse over the points and see how many drone strikes were done each year. During Obama's presidency the drone strikes in Pakistan drastically increased. Yemen also saw an increase in drone strikes, while Somalia has had a decrease in the number of drone strikes.
plot1 <- t_casualties %>% ggplot(aes(x = fct_reorder(Country, total_civ_deaths), y = total_civ_deaths, fill = Country)) +
geom_col() + geom_text(aes(x = Country, y = total_civ_deaths + 100 , label = round(total_civ_deaths),vjust=1),col = "black") + theme_bw() + labs(title = "Total Civilian casualties from drone strikes per country", x = "Country", y = "Casualties")
plot2 <- t_casualties %>% ggplot(aes(x = fct_reorder(Country, total_child_deaths), y = total_child_deaths, fill = Country)) +
geom_col() + geom_text(aes(x = Country, y = total_child_deaths + 100 , label = round(total_child_deaths),vjust=4.5),col = "black") + theme_bw() + labs(title = "Children casualties from drone strikes per country", x = "Country", y = "Casualties")
grid.arrange(plot1,plot2)
These two bar charts show the total number of civilian casualties and children casualties in each country. The children casualties are accounted for in the total civilian casualties. From the data the most civilian casualties come from Pakistan and Yemen, which are the 2 countries that had the most drone strikes
cas_per_yr <- cas_per_yr %>% mutate(Year = as.numeric(year))
p <- cas_per_yr %>% group_by(Year) %>% ggplot(aes(x = Year, y = Civ_casualties, col = Country)) +
geom_point() +
geom_line() +
theme_ipsum() +
facet_wrap(~Country, scales = "free_y") + labs(title = "(Interactive) Civilian Casualties per Country", x = "Year", y = "Casualties")
ggplotly(p)
The plot above shows the different years of each country in the data set and how many casualties happened in a specific year. There was a significant increasing trend in Pakistan from 2007-2012. There was also a increase in casualties in Somalia where the highest rate was in 2017. While in Yemen the highest amount of casualties occurred in 2012
table_t_cas <- t_casualties %>%
rename("Civilian_casualties" = mean_Civ_casualties, "Children_Casualties" = mean_childen_casualties) %>%
mutate("Civilian_casualties" = round(as.numeric(Civilian_casualties),2), Children_Casualties = round(as.numeric(Children_Casualties),2))
datatable(table_t_cas)
The table above shows the average civilian and child deaths per drone strike. It also includes the total deaths of civilians and children.
Af %>% group_by(Reported.target.group.) %>%
rename(target_group = Reported.target.group.)%>%
filter(!target_group %in% c("-", "Friendly fire", "Friendly Fire")) %>%
mutate(Count = n()) %>%
ggplot(aes(x = fct_reorder(target_group, Count))) +
geom_bar(fill = "skyblue") + geom_text(stat='count', aes(label=..count.., hjust=-.1),col = "black")+ coord_flip() +
labs(title = "Targeted Groups", x ="Targeted groups" , y ="Count") + theme_bw()
This chart shows the most targeted group that was included in the Afghanistan data-set. These were the most targeted group in general, not necessarily by only drone strikes.
In conclusion the data above shows: